There are well-established gaps in the American healthcare system for minority populations.
Groups that have been historically marginalized have also had worse treatment options and longitudinal health outcomes.
Biases are especially troubling in the context of machine learning applied to clinical data. Bias can be replicated and exacerbated in the model's future recommendations [1]}. For example, black and Hispanic patients are often given less pain medication for equivalent injuries and reported pain levels [2]}, [3]}. If this pattern is present in the training data for a model built to recommend treatment, it would learn to associate race with pain medication dosage.
